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Compute a rolling quantile based on another column

Source code

Description

Compute a rolling quantile based on another column

Usage

<Expr>$rolling_quantile_by(
  by,
  window_size,
  ...,
  quantile,
  interpolation = "nearest",
  min_periods = 1,
  closed = "right"
)

Arguments

by This column must of dtype Date or Datetime.
window_size The length of the window. Can be a fixed integer size, or a dynamic temporal size indicated by the following string language:
  • 1ns (1 nanosecond)
  • 1us (1 microsecond)
  • 1ms (1 millisecond)
  • 1s (1 second)
  • 1m (1 minute)
  • 1h (1 hour)
  • 1d (1 day)
  • 1w (1 week)
  • 1mo (1 calendar month)
  • 1y (1 calendar year)
  • 1i (1 index count) If the dynamic string language is used, the by and closed arguments must also be set.
… Ignored.
quantile Either a numeric value or an Expr whose value must be between 0 and 1.
interpolation One of “nearest”, “higher”, “lower”, “midpoint”, or “linear”.
min_periods The number of values in the window that should be non-null before computing a result. If NULL, it will be set equal to window size.
closed Define which sides of the temporal interval are closed (inclusive). This can be either “left”, “right”, “both” or “none”.

Details

If you want to compute multiple aggregation statistics over the same dynamic window, consider using $rolling() this method can cache the window size computation.

Value

Expr

Examples

library("polars")

df_temporal = pl$DataFrame(
  date = pl$datetime_range(as.Date("2001-1-1"), as.Date("2001-1-2"), "1h")
)$with_row_index("index")

df_temporal

#> shape: (25, 2)
#> ┌───────┬─────────────────────┐
#> │ index ┆ date                │
#> │ ---   ┆ ---                 │
#> │ u32   ┆ datetime[μs]        │
#> ╞═══════╪═════════════════════╡
#> │ 0     ┆ 2001-01-01 00:00:00 │
#> │ 1     ┆ 2001-01-01 01:00:00 │
#> │ 2     ┆ 2001-01-01 02:00:00 │
#> │ 3     ┆ 2001-01-01 03:00:00 │
#> │ 4     ┆ 2001-01-01 04:00:00 │
#> │ …     ┆ …                   │
#> │ 20    ┆ 2001-01-01 20:00:00 │
#> │ 21    ┆ 2001-01-01 21:00:00 │
#> │ 22    ┆ 2001-01-01 22:00:00 │
#> │ 23    ┆ 2001-01-01 23:00:00 │
#> │ 24    ┆ 2001-01-02 00:00:00 │
#> └───────┴─────────────────────┘

df_temporal$with_columns(
  rolling_row_quantile = pl$col("index")$rolling_quantile_by(
    "date",
    window_size = "2h", quantile = 0.3
  )
)

#> shape: (25, 3)
#> ┌───────┬─────────────────────┬──────────────────────┐
#> │ index ┆ date                ┆ rolling_row_quantile │
#> │ ---   ┆ ---                 ┆ ---                  │
#> │ u32   ┆ datetime[μs]        ┆ f64                  │
#> ╞═══════╪═════════════════════╪══════════════════════╡
#> │ 0     ┆ 2001-01-01 00:00:00 ┆ 0.0                  │
#> │ 1     ┆ 2001-01-01 01:00:00 ┆ 0.0                  │
#> │ 2     ┆ 2001-01-01 02:00:00 ┆ 1.0                  │
#> │ 3     ┆ 2001-01-01 03:00:00 ┆ 2.0                  │
#> │ 4     ┆ 2001-01-01 04:00:00 ┆ 3.0                  │
#> │ …     ┆ …                   ┆ …                    │
#> │ 20    ┆ 2001-01-01 20:00:00 ┆ 19.0                 │
#> │ 21    ┆ 2001-01-01 21:00:00 ┆ 20.0                 │
#> │ 22    ┆ 2001-01-01 22:00:00 ┆ 21.0                 │
#> │ 23    ┆ 2001-01-01 23:00:00 ┆ 22.0                 │
#> │ 24    ┆ 2001-01-02 00:00:00 ┆ 23.0                 │
#> └───────┴─────────────────────┴──────────────────────┘